CN110262408A - A kind of intelligent storage route identification device and method for more AGV - Google Patents
A kind of intelligent storage route identification device and method for more AGV Download PDFInfo
- Publication number
- CN110262408A CN110262408A CN201910378416.XA CN201910378416A CN110262408A CN 110262408 A CN110262408 A CN 110262408A CN 201910378416 A CN201910378416 A CN 201910378416A CN 110262408 A CN110262408 A CN 110262408A
- Authority
- CN
- China
- Prior art keywords
- agv
- time
- path
- node
- section
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 33
- 238000012545 processing Methods 0.000 claims abstract description 25
- 238000004891 communication Methods 0.000 claims abstract description 13
- 230000007613 environmental effect Effects 0.000 claims abstract description 8
- 230000005540 biological transmission Effects 0.000 claims abstract description 7
- 238000000605 extraction Methods 0.000 claims abstract description 7
- 230000003993 interaction Effects 0.000 claims abstract description 4
- 239000003016 pheromone Substances 0.000 claims description 38
- 239000011159 matrix material Substances 0.000 claims description 15
- 230000006872 improvement Effects 0.000 claims description 9
- 238000012544 monitoring process Methods 0.000 claims description 8
- 238000005457 optimization Methods 0.000 claims description 7
- 238000013439 planning Methods 0.000 claims description 7
- 230000002265 prevention Effects 0.000 claims description 7
- 238000012360 testing method Methods 0.000 claims description 7
- 230000008859 change Effects 0.000 claims description 5
- 238000007254 oxidation reaction Methods 0.000 claims description 4
- 230000003068 static effect Effects 0.000 claims description 4
- 238000001514 detection method Methods 0.000 claims description 3
- 238000013024 troubleshooting Methods 0.000 claims description 3
- 230000002452 interceptive effect Effects 0.000 claims description 2
- 230000004888 barrier function Effects 0.000 claims 1
- 238000005530 etching Methods 0.000 claims 1
- 239000004744 fabric Substances 0.000 claims 1
- 210000004209 hair Anatomy 0.000 claims 1
- 230000001172 regenerating effect Effects 0.000 abstract description 3
- 230000004044 response Effects 0.000 abstract description 3
- 239000000463 material Substances 0.000 description 5
- 230000008569 process Effects 0.000 description 4
- 230000008901 benefit Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 235000003642 hunger Nutrition 0.000 description 2
- 239000000203 mixture Substances 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 230000037351 starvation Effects 0.000 description 2
- 241000257303 Hymenoptera Species 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000000903 blocking effect Effects 0.000 description 1
- 230000001186 cumulative effect Effects 0.000 description 1
- 238000013501 data transformation Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000005553 drilling Methods 0.000 description 1
- 235000013399 edible fruits Nutrition 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 230000005484 gravity Effects 0.000 description 1
- 238000009434 installation Methods 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 238000002360 preparation method Methods 0.000 description 1
- 238000012797 qualification Methods 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 238000013468 resource allocation Methods 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
- 238000012384 transportation and delivery Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
- G05B19/4189—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by the transport system
- G05B19/41895—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by the transport system using automatic guided vehicles [AGV]
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/30—Nc systems
- G05B2219/31—From computer integrated manufacturing till monitoring
- G05B2219/31086—Communication of carriage, agv data, workpiece data at each station
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/60—Electric or hybrid propulsion means for production processes
Landscapes
- Engineering & Computer Science (AREA)
- General Engineering & Computer Science (AREA)
- Manufacturing & Machinery (AREA)
- Quality & Reliability (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Traffic Control Systems (AREA)
Abstract
The invention discloses a kind of intelligent storage route identification device and method for more AGV, S100, off-line phase AGV vehicle shortest path is determined, improve the real-time of system, reduce the burden of scheduling system on-line operation；S200, optimal scheduling scheme is generated；S300, system real-time running state is obtained, different penalty factor processing, and the real time information of extraction system is subject to when calculating scheduling scheme fitness value；S400, the Dynamic Scheduling Strategy for implementing more AGV, the real-time status of whole system is grasped using time window；S500, it realizes communication interaction system, establishes reliable communication connection between AGV scheduling system and AGV vehicle, so as to the AGV operating status and environmental information for transmitting the order of host computer transmission, the next function is collected into；Whole system is conducive to balance because regenerating the time consumed by offline shortest path library, while being easy to implement system when section is broken down and realizing quick response, improves scheduling scheme fitness value, effectively increases the efficiency and stability of system.
Description
Technical field
The present invention relates to storage route identification technology field, specially a kind of intelligent storage route for more AGV is identified
Device and method.
Background technique
Automatic Material Handling System refers to when in specific room and time, by need equipment, material, storage facilities, personnel,
Multiple dynamic factor compositions mutually restricted such as communication contact and means of delivery, are that the having with specific function is whole.
It is by transfer mechanism system, automatic stereo warehouse, computer management system, automatic guided vehicle (AGV) system and automatically controlled system
System composition, is the high-tech industry of a modernization across the epoch.
Demand with material flows automation, intellectualizing system increasingly increases, and the domestic demand to automatic guided vehicle is also gradually
Cumulative length.In entire automated warehousing logistics, AGV transportation cost accounts for that totle drilling cost specific gravity is higher, the accurate scheduling for task of storing in a warehouse
And the good Path Planning of AGV, to improving, logistics operation efficiency, reduction transportation cost are significant.
AGV scheduling theory refers to the scheduling in storage workshop to the scheduling of transport operation and to AGV vehicle.Good work
Industry scheduling strategy can make logistic storage task deadlock, task starvation situation avoid occurring as far as possible；Good AGV trolley tune
Degree can make logistic storage task complete in time, and hand-in-glove between AGV trolley can be greatly improved utilization rate of equipment and installations, subtract
Few goods and materials cost.Domestic and foreign scholars to warehouse logistics workshop AGV scheduling optimizing in terms of done many researchs, achieve it is many at
Fruit, these achievements have directive function to the design of AGV scheduling system, can be effectively reduced task deadlock and it is hungry occur it is general
Rate increases productivity to enterprise, reduces human cost, improves device resource utilization rate, improving the core competitiveness of enterprise, there have to be important
Meaning.
Although many research achievements about AGV scheduling can provide different Lothrus apterus for multiple AGV of friction speed
Dynamic obstacle avoidance may be implemented in route.But path conflict type and catastrophic failure is referred to less, are unable to satisfy fairly large system
Scene demand, when task and very more vehicle, be easy to produce task starvation.
But there is also following defects for the intelligent storage route identification device and method for being currently used for more AGV:
Although the mutually coordinated ability Yuan Yuan Qe of more AGV systems is in separate unit AGV system, there is also more AGV systems
Distinctive some problems: (1) conflict between AGV how is solved；(2) how to ensure the behavior coordination between AGV；(3) how
Realize the information sharing between AGV.
Summary of the invention
In order to overcome the shortcomings of that prior art, the present invention provide intelligent storage route of the kind for more AGV and know
Other device and method, the present invention is different from traditional extracting method, and it is real-time to solve system using two stages Job-Shop system
Operating status obtains, offline shortest path library generates, three key technologies of online optimal scheduling schemes generation, reduces previous straight
Difficulty in computation caused by time window collision prevention was connected, and keeps system running state more transparent controllable, meanwhile, pass through algorithm model
Routing strategy is optimized in the case where comprehensively considering runing time, stop frequency, number of turns, is solved more
AGV Real-Time Scheduling problem can effectively solve the problem of background technique proposes.
The technical solution adopted by the present invention to solve the technical problems is:
A kind of intelligent storage route recognition methods for more AGV, includes the following steps:
S100, determine that off-line phase AGV vehicle shortest path, first extraction path coordinate, node relationships establish entitled nothing
To graph model, then it is based on the online path planning of time window real-time perfoming, realizes collision prevention, improve the real-time of system, reduces scheduling
The burden of system on-line operation；
S200, generate optimal scheduling scheme, in conjunction with each node in traffic rules method, prediction type collision prevention method control system,
On section AGV by the time, through micro-oxidation test the case where comprehensively considering runing time, stop frequency, number of turns
Under routing scheme is optimized；
S300, system real-time running state is obtained, the burden for comprehensively considering parking, turning to AGV mechanical structure and motor,
It is subject to different penalty factor processing, and the real time information of extraction system when calculating scheduling scheme fitness value；
S400, the Dynamic Scheduling Strategy for implementing more AGV, the real-time status of whole system are grasped using time window, according to more
The real time status information of a AGV vehicle realizes vehicle Dynamic Scheduling Strategy；
S500, it realizes communication interaction system, establishes reliable communication connection between AGV scheduling system and AGV vehicle, so as to
The AGV operating status and environmental information that the order of transmission host computer transmission, the next function are collected into.
Further, in the S100, further includes:
S101, according to workshop map interior joint coordinate information, node relationships information, first generating includes the practical letter in workshop section
Cease adjacency matrix；
S102 finds out the shortest path of start site i to targeted sites j using improvement ant group algorithm model, and will be corresponding
Nodal information is stored in comprising in the information matrix of path library, the information of record includes section interior joint number, section number, section
Length, wherein storage form is path (i, j, 1), and be expressed as start site i to targeted sites j searches the first paths；
S103 counts current alternative path number, judges whether current alternative path number is greater than the alternative path of setting
Number maximum value is to turn to S104, otherwise turns to S105；
S104 is ranked up current path collection L by length, rejects the longest path of path length, guarantees alternative path
Number is preset number, improves search efficiency when on-line scheduling；
S105 judges whether to continue to generate new section according to the road section information on shortest path, is to turn to S106,
Otherwise S107 is turned to；
S106 successively deletes a line of shortest path in website i to website j, i.e. successively one in shortest path
Edge lengths are ∞.It calls improvement ant group algorithm model to find out current shortest path I under new adjacency matrix, turns to S103；
S107 judges the value of start site i and targeted sites j, and i≤N, j < N, i is constant, j=j+1；When i < N, j=N, i
=i+1, j=1 turns to S102；
S108, offline path library generate successfully, and generate path in first be shortest path, in remaining path
Include secondary short circuit warp.
Further, ant group algorithm model is improved in the S102 are as follows:
S1021, it is assumed that the ant total amount of entire ant colony is m, and the number of current ant is k, the letter on t moment side (i, j)
Ceasing plain concentration is Δ τij(t), ant k is every is by an iteration Pheromone update amount
S1022 obtains local information element according to the more new strategy of pheromones are as follows::
Wherein, Q is pheromones total amount, is a constant；LkFor the total distance passed through during current ant current iteration,
θ is that pheromones increase the factor, and value range is (0.1)
Pheromone concentration on every road is arranged at [min, max] S1023, between, and pheromones volatilization is drawn
Enter to assist volatilization factor, to global information element more new strategy are as follows:
Wherein, it adds parameter ω ∈ (0,1) and γ ∈ (1,2), ω is the negative volatilization factor of pheromones, γ is that pheromones are just waved
Send out the factor.
S1024, when present road pheromone concentration is less than road information element concentration limit, system can be by present road information
Plain concentration is set to road information element concentration minimum value.
Further, the S200 further include:
S201 generates the time for ideally describing AGV operating status according to AGV mission bit stream and scheduling scheme first
Window, including occupying the information such as node, the AGV number in section, initial time, end time, time window length；
S202, by generating node time window, the mode for preventing AGV from generating overlapping in node time window avoids node conflict
Occur, including conflicts in opposite directions and right-angled intersection node conflict；
S203 sets AGV as at the uniform velocity to avoid coming up with conflict, by generating section time window, prevents AGV in section
The mode that time window generates overlapping avoids section from conflicting in opposite directions generation；
S204 is ranked up the node time window and section time window of current all overlappings, wherein section time window
Search the conllinear and contrary section of route.
S205 repeats the operation of S202, S203 and S204, until AGV is on all node time windows, section time window
It is non-overlapping, terminate to search update operation；
S206 counts the time that each AGV reaches destination node, take wherein the runing time longest time be used as dispatching party
The case overall operation time.
Further, the S200 further include: the path runing time according to obtained in S200 performs the following operation:
S207, according to the adjacency matrix table of relationship between expression section and node serial number rule, window process is got the bid between generation
Section position is parallel or vertical between caravan, thus according to AGV in the position enquiring statistics scheduling scheme between the section of front and back
The number of turning；
S208, according to search overlapping time window after to time window into the statistics of the number ingeniously updated, to calculate tune
The number that AGV is waited for parking in degree scheme；
S209 realizes scheduling scheme using following formula:
Wherein, trunFor runing time, aL is AGV vehicle commander, and av is AGV speed, trurnFor number of turns, SstopStop for AGV
Train number number.
Further, in the S300 further include:
S301 determines the boundary route range of each AGV first with the wireless sensor network inside AGV；
Then S302 is closed in boundary route range according to the requirement of the size of detection zone, site environment and positioning accuracy
Removing the work sets beaconing nodes, and numbers, and forms beaconing nodes library；
S303 sends broadcast packet, all beacon sections for receiving this broadcast packet to surrounding after node access networks network to be measured
Point sends broadcast packet and gives the node to be measured；
S304 sorts beaconing nodes according to the sequence of RSSI value from high to low, and preferably preceding n beaconing nodes are fixed for this
The reference mode of position reads its number and corresponding RSSI value:
RSSI=Pi-PL(d)；
Wherein, PL (d) is the path loss after distance d, d0For reference distance,It is by reference distance
d0Path loss afterwards.
Further, in the S400 further include:
S401, under workshop normal operating conditions, it is assumed that t moment system monitoring is sent out to there are AGV at some node
Raw failure can be obtained normal condition dispatching system by test and complete transmitting order to lower levels to AGV institute to calculating from monitoring failure
The time t ' needed, and inquire the locating time window for generating the AGV of failure at the t+t ' moment；
S402, after there is AGV failure at workshop node, remaining is carrying out the AGV of scheduler task at the t+t ' moment
Locating time window have and only there are three types of, malfunctioning node is connected section time window, normal node time window and normal section time
Window；
S403, workshop dispatching system monitors there are AGV on some section to after breaking down, according to system environments
Real time monitoring information and the Real-time Feedback of AGV judge whether failure AGV task is completed, completion does not need New School's vehicle then,
Otherwise it inquires and whether troubleshooting is waited to complete, waiting wouldn't then operate the task, and whether otherwise inquire has empty wagons, without then not
Group, has, sends out new car；
S404 inquires workshop environmental information and the operating status of AGV, determines the t+t ' moment according to S402, S403
The scheduler task of system, and generate specific task scheduling table；
S405, when workshop node breaks down, the shortest path library failure that original generates offline needs to be believed according to failure
The node relationships updated in workshop map again, adjacency matrix are ceased, the section distance that node is connected are set as infinite, and right
The generating algorithm in shortest path library adjusts, and generates the new offline path library for meeting workshop situation, timely responds to system change
Change
S406, the improvement ant group algorithm model for calling static state workshop two-stage AGV scheduling to solve are calculated；
S407, after optimization, scheduling system generate optimal scheduling scheme, and export the scheduling scheme fitness value,
Overall operation time, stop frequency, number of turns and each AGV are in its planning path by node, the specific time in section
Window information.
In addition the present invention also provides a kind of intelligent storage route identification device for claim 1 the method, packets
Include data acquisition module, database, data processing unit and AGV control module；
The data terminal and database of the data acquisition module interconnect, the data acquisition module output AGV scheduling
Information is handled to data processing unit；
The data processing unit realizes the processing to more AGV scheduling strategy information using improved Ant Colony System, and will
Processing information is transmitted to AGV control module；
The AGV control module identifies road according to the location information of the processing information and self-sensor device network received
Line；
The communication port of the AGV control module, which also passes through wireless network connection, host computer, the AGV control module
Control terminal, which also interconnects, radio frequency control module.
Compared with prior art, the beneficial effects of the present invention are:
The present invention is different from traditional route recognition methods, when whole system breaks down according to workshop in shortest path library
AGV alternative path number reduce, so as to cause when initialization of population individual combination variety reduce, path optimization's calculating process receive
Phenomena such as speed is accelerated is held back, balance is easy to implement section and goes out because regenerating the time consumed by offline shortest path library
System realizes quick response when existing failure, improves scheduling scheme fitness value, effectively increases the efficiency and stability of system.
Detailed description of the invention
Fig. 1 is flow chart of the method for the present invention；
Fig. 2 is structural block diagram of the invention；
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts every other
Embodiment shall fall within the protection scope of the present invention.
As shown in Fig. 2, the present invention provides a kind of intelligent storage route identification device for more AGV, including data obtain
Modulus block, database, data processing unit and AGV control module；The data terminal of the data acquisition module is interacted with database
Connection, the data acquisition module output AGV scheduling information are handled to data processing unit；The data processing unit benefit
The processing to more AGV scheduling strategy information is realized with improved Ant Colony System, and processing information is transmitted to AGV control module；
The AGV control module identifies route according to the location information of the processing information and self-sensor device network received；It is described
The communication port of AGV control module, which also passes through wireless network connection, host computer, and the control terminal of the AGV control module is also interactive
It is connected with radio frequency control module.
In the present embodiment, is dispatched in AGV and establish reliable communication connection between system and AGV vehicle, it is upper to transmit
The AGV operating status and environmental information that the order of machine transmission, slave computer are collected into, the present invention select the UDP in OSI Reference Model
Agreement carries out wireless module and transmits data, passes through UDP control block and related application interface letter using ESP8266 serial ports wireless module
Number realizes data-transformation facilities, by AGV control module, data processing unit, data acquisition module static Job-Shop
Situation calls two-stage strategy dispatching algorithm solving optimization path.
AGV alternative path number in the present embodiment, when AGV control module breaks down according to workshop in shortest path library
Reduce, so as to cause when initialization of population individual combination variety reduce, path optimization's calculating process convergence rate accelerate phenomena such as,
Balance is easy to implement system when section is broken down and realizes fastly because regenerating the time consumed by offline shortest path library
Speed response, improves scheduling scheme fitness value, effectively increases the efficiency and stability of system.
In the present embodiment, it as shown in Figure 1, being used for the intelligent storage route recognition methods of more AGV, including walks as follows
It is rapid:
S100, determine that off-line phase AGV vehicle shortest path, first extraction path coordinate, node relationships establish entitled nothing
To graph model, then it is based on the online path planning of time window real-time perfoming, realizes collision prevention, improve the real-time of system, reduces scheduling
The burden of system on-line operation；
S200, generate optimal scheduling scheme, in conjunction with each node in traffic rules method, prediction type collision prevention method control system,
On section AGV by the time, through micro-oxidation test the case where comprehensively considering runing time, stop frequency, number of turns
Under routing scheme is optimized；
S300, system real-time running state is obtained, the burden for comprehensively considering parking, turning to AGV mechanical structure and motor,
It is subject to different penalty factor processing, and the real time information of extraction system when calculating scheduling scheme fitness value；
S400, the Dynamic Scheduling Strategy for implementing more AGV, the real-time status of whole system are grasped using time window, according to more
The real time status information of a AGV vehicle realizes vehicle Dynamic Scheduling Strategy；
S500, it realizes communication interaction system, establishes reliable communication connection between AGV scheduling system and AGV vehicle, so as to
The AGV operating status and environmental information that the order of transmission host computer transmission, the next function are collected into.
In the present embodiment, about the Mission Scheduling of AGV, i.e., in logistics transportation system, if there are several AGV,
Dry working terminal and materials warehouse website, it is desirable that reasonably transport task and AGV vehicle are allocated as far as possible, thus
Under the qualifications such as operating path, runing time, vehicle performance, meet certain regulation index, complete scheduler task, resit an exam
AGV resource allocation problem is considered, including to space resources and time reasonable arrangement, has coordinated AGV vehicle in garage, make in system
AGV reaches target endpoint in the case where evaluation index is optimal.
In the present embodiment, conventional two-stage solution strategies make improvement, wherein mainly there are three aspects in improved place:
(1) the more AGV systems of concept calculating using AGV time window, node time window, the more time windows of section time window are given
The runing time that completion scheduler task needs under path, and it is unconventional using time window processing collision problem, it solves nothing and touches path；
(2) using the operating path of improved ant group algorithm optimization AGV, Job-Shop alternative path scheme is limited, solves
Rate request is high, matches very much, is fully considered in Job-Shop system with the characteristic of micro-oxidation test small sample, fast convergence
The characteristic of real-time height, alternative path Limited Number when AGV Path selection；(3) in addition to considering that operation is most short on AGV regulation index
Outside time and shortest path, parking, turning have been comprehensively considered to the burden of AGV mechanical structure and motor, it is suitable to calculate scheduling scheme
It is subject to different penalty factor processing when answering angle value, effectively raises the efficiency and stability of system.
In the S100, further includes:
S101, according to workshop map interior joint coordinate information, node relationships information, first generating includes the practical letter in workshop section
Cease adjacency matrix；
S102 finds out the shortest path of start site i to targeted sites j using improvement ant group algorithm model, and will be corresponding
Nodal information is stored in comprising in the information matrix of path library, the information of record includes section interior joint number, section number, section
Length, wherein storage form is path (i, j, 1), and be expressed as start site i to targeted sites j searches the first paths；
S103 counts current alternative path number, judges whether current alternative path number is greater than the alternative path of setting
Number maximum value is to turn to S104, otherwise turns to S105；
S104 is ranked up current path collection L by length, rejects the longest path of path length, guarantees alternative path
Number is preset number, improves search efficiency when on-line scheduling；
S105 judges whether to continue to generate new section according to the road section information on shortest path, is to turn to S106,
Otherwise S107 is turned to；
S106 successively deletes a line of shortest path in website i to website j, i.e. successively one in shortest path
Edge lengths are ∞.It calls improvement ant group algorithm model to find out current shortest path I under new adjacency matrix, turns to S103；
S107 judges the value of start site i and targeted sites j, and i≤N, j < N, i is constant, j=j+1；When i < N, j=N, i
=i+1, j=1 turns to S102；
S108, offline path library generate successfully, and generate path in first be shortest path, in remaining path
Include secondary short circuit warp.
Ant group algorithm model is improved in the S102 are as follows:
S1021, it is assumed that the ant total amount of entire ant colony is m, and the number of current ant is k, the letter on t moment side (i, j)
Ceasing plain concentration is Δ τij(t), ant k is every is by an iteration Pheromone update amount
S1022 obtains local information element according to the more new strategy of pheromones are as follows::
Wherein, Q is pheromones total amount, is a constant；LkFor the total distance passed through during current ant current iteration,
θ is that pheromones increase the factor, and value range is (0.1)
Pheromone concentration on every road is arranged at [min, max] S1023, between, and pheromones volatilization is drawn
Enter to assist volatilization factor, to global information element more new strategy are as follows:
Wherein, it adds parameter ω ∈ (0,1) and γ ∈ (1,2), ω is the negative volatilization factor of pheromones, γ is that pheromones are just waved
Send out the factor.
S1024, when present road pheromone concentration is less than road information element concentration limit, system can be by present road information
Plain concentration is set to road information element concentration minimum value.
In the present embodiment, during path-search, the pheromone concentration on road can volatilize ant according to given pace, if certain
Pheromones on road persistently evaporate into 0, and ant will stop shifting to this paths, in order to avoid ant falls into local optimum
Solution, by every road pheromone concentration be arranged between [min, max], and to pheromones volatilize introduce auxiliary volatilization because
Son.
In the present embodiment, when the distance that ants predictive algorithm is passed by oneself is greater than the Euclidean distance of starting point and point of destination
When, just accelerate the pheromone concentration on present road, meanwhile, in order to avoid algorithm falls into local optimum, the letter on every road
A certain range can all be maintained by ceasing plain concentration, if present road pheromone concentration is less than road information element concentration limit, be
Present road pheromone concentration can be set to road information element concentration minimum value by system.
In the present embodiment, in pheromones volatilization strategy, provide intelligent function for ant, ant beginning searching route it
Between, a valuation D first is done to the Euclidean distance of starting point to point of destination0, after ant pathfinding starts, pass through when ant discovery
D when distance D is greater than0, illustrate that current path is not shortest path, in order to reduce algorithm iteration number, intellectual ant will be mentioned suitably
The pheromones volatilization factor of high present road accelerates the algorithm speed of service.When the distance D that ant discovery is passed through is less than D0When, then
Present road pheromones volatilization factor is reduced, to guarantee that algorithm can find shortest path.
The S200 further include:
S201 generates the time for ideally describing AGV operating status according to AGV mission bit stream and scheduling scheme first
Window, including occupying the information such as node, the AGV number in section, initial time, end time, time window length；
S202, by generating node time window, the mode for preventing AGV from generating overlapping in node time window avoids node conflict
Occur, including conflicts in opposite directions and right-angled intersection node conflict；
S203 sets AGV as at the uniform velocity to avoid coming up with conflict, by generating section time window, prevents AGV in section
The mode that time window generates overlapping avoids section from conflicting in opposite directions generation；
S204 is ranked up the node time window and section time window of current all overlappings, wherein section time window
Search the conllinear and contrary section of route.
S205 repeats the operation of S202, S203 and S204, until AGV is on all node time windows, section time window
It is non-overlapping, terminate to search update operation；
S206 counts the time that each AGV reaches destination node, take wherein the runing time longest time be used as dispatching party
The case overall operation time.
The S200 further include: the path runing time according to obtained in S200 performs the following operation:
S207, according to the adjacency matrix table of relationship between expression section and node serial number rule, window process is got the bid between generation
Section position is parallel or vertical between caravan, thus according to AGV in the position enquiring statistics scheduling scheme between the section of front and back
The number of turning；
S208, according to search overlapping time window after to time window into the statistics of the number ingeniously updated, to calculate tune
The number that AGV is waited for parking in degree scheme；
S209 realizes scheduling scheme using following formula:
Wherein, trunFor runing time, aL is AGV vehicle commander, and av is AGV speed, trurnFor number of turns, SstopStop for AGV
Train number number.
In the S300 further include:
S301 determines the boundary route range of each AGV first with the wireless sensor network inside AGV；
Then S302 is closed in boundary route range according to the requirement of the size of detection zone, site environment and positioning accuracy
Removing the work sets beaconing nodes, and numbers, and forms beaconing nodes library；
S303 sends broadcast packet, all beacon sections for receiving this broadcast packet to surrounding after node access networks network to be measured
Point sends broadcast packet and gives the node to be measured；
S304 sorts beaconing nodes according to the sequence of RSSI value from high to low, and preferably preceding n beaconing nodes are fixed for this
The reference mode of position reads its number and corresponding RSSI value:
RSSI=Pi-PL(d)；
Wherein, PL (d) is the path loss after distance d, d0For reference distance,It is by reference distance
d0Path loss afterwards.
In the S400 further include:
S401, under workshop normal operating conditions, it is assumed that t moment system monitoring is sent out to there are AGV at some node
Raw failure can be obtained normal condition dispatching system by test and complete transmitting order to lower levels to AGV institute to calculating from monitoring failure
The time t ' needed, and inquire the locating time window for generating the AGV of failure at the t+t ' moment；
S402, after there is AGV failure at workshop node, remaining is carrying out the AGV of scheduler task at the t+t ' moment
Locating time window have and only there are three types of, malfunctioning node is connected section time window, normal node time window and normal section time
Window；
S403, workshop dispatching system monitors there are AGV on some section to after breaking down, according to system environments
Real time monitoring information and the Real-time Feedback of AGV judge whether failure AGV task is completed, completion does not need New School's vehicle then,
Otherwise it inquires and whether troubleshooting is waited to complete, waiting wouldn't then operate the task, and whether otherwise inquire has empty wagons, without then not
Group, has, sends out new car；
S404 inquires workshop environmental information and the operating status of AGV, determines the t+t ' moment according to S402, S403
The scheduler task of system, and generate specific task scheduling table；
S405, when workshop node breaks down, the shortest path library failure that original generates offline needs to be believed according to failure
The node relationships updated in workshop map again, adjacency matrix are ceased, the section distance that node is connected are set as infinite, and right
The generating algorithm in shortest path library adjusts, and generates the new offline path library for meeting workshop situation, timely responds to system change
Change
S406, the improvement ant group algorithm model for calling static state workshop two-stage AGV scheduling to solve are calculated；
S407, after optimization, scheduling system generate optimal scheduling scheme, and export the scheduling scheme fitness value,
Overall operation time, stop frequency, number of turns and each AGV are in its planning path by node, the specific time in section
Window information.
In the present embodiment, main feature is as follows:
The present invention is different from traditional route recognition methods:
(1) it ensure that between all start-stop websites there is the route that can be reached on path；
(2) AGV is avoided on the route planned as far as possible and node, section conflict occurs, reduce the phenomenon that blocking；
(3) the whole path travel time that the route planned as far as possible completes scheduler task is shorter, to improve system
Operational efficiency；
(4) reduce stop frequency, number of turns, reduce the burden of AGV mechanical structure and motor, improve efficiency.
It is obvious to a person skilled in the art that invention is not limited to the details of the above exemplary embodiments, Er Qie
In the case where without departing substantially from spirit or essential attributes of the invention, the present invention can be realized in other specific forms.Therefore, no matter
From the point of view of which point, the present embodiments are to be considered as illustrative and not restrictive, and the scope of the present invention is by appended power
Benefit requires rather than above description limits, it is intended that all by what is fallen within the meaning and scope of the equivalent elements of the claims
Variation is included within the present invention.Any reference signs in the claims should not be construed as limiting the involved claims.
Claims (8)
1. a kind of intelligent storage route recognition methods for more AGV, characterized by the following steps:
S100, determine that off-line phase AGV vehicle shortest path, first extraction path coordinate, node relationships establish entitled non-directed graph
Model, then it is based on the online path planning of time window real-time perfoming, it realizes collision prevention, improves the real-time of system, reduce scheduling system
The burden of on-line operation；
S200, optimal scheduling scheme is generated, in conjunction with each node, section in traffic rules method, prediction type collision prevention method control system
Upper AGV by the time, it is right in the case where comprehensively considering runing time, stop frequency, number of turns by micro-oxidation test
Routing scheme optimizes；
S300, system real-time running state is obtained, comprehensively considers parking, turning to the burden of AGV mechanical structure and motor, calculates
It is subject to different penalty factor processing, and the real time information of extraction system when scheduling scheme fitness value；
S400, the Dynamic Scheduling Strategy for implementing more AGV, the real-time status of whole system are grasped using time window, according to multiple AGV
The real time status information of vehicle realizes vehicle Dynamic Scheduling Strategy；
S500, it realizes communication interaction system, reliable communication connection is established between AGV scheduling system and AGV vehicle, to transmit
The AGV operating status and environmental information that the order of host computer transmission, the next function are collected into.
2. a kind of intelligent storage route recognition methods for more AGV according to claim 1, it is characterised in that: described
In S100, further includes:
S101, according to workshop map interior joint coordinate information, node relationships information is first generated adjacent comprising workshop section actual information
Connect matrix；
S102 finds out the shortest path of start site i to targeted sites j using ant group algorithm model is improved, and by respective nodes
Information is stored in comprising in the information matrix of path library, and the information of record includes that section interior joint number, section number, section are long
Degree, wherein storage form is path (i, j, 1), and be expressed as start site i to targeted sites j searches the first paths；
S103 counts current alternative path number, judges whether current alternative path number is greater than the alternative path number of setting
Maximum value is to turn to S104, otherwise turns to S105；
S104 is ranked up current path collection L by length, rejects the longest path of path length, guarantees that alternative path number is
Preset number improves search efficiency when on-line scheduling；
S105 judges whether to continue to generate new section according to the road section information on shortest path, is to turn to S106, otherwise
Turn to S107；
S106, successively deletes a line of shortest path in website i to website j, i.e. a line successively in shortest path is long
Degree is ∞.It calls improvement ant group algorithm model to find out current shortest path I under new adjacency matrix, turns to S103；
S107 judges the value of start site i and targeted sites j, and i≤N, j < N, i is constant, j=j+1；When i < N, j=N, i=i+
1, j=1, turn to S102；
S108, offline path library generate successfully, and generate path in first be shortest path, include in remaining path
Secondary short circuit warp.
3. a kind of intelligent storage route recognition methods for more AGV according to claim 1, it is characterised in that: described
Ant group algorithm model is improved in S102 are as follows:
S1021, it is assumed that the ant total amount of entire ant colony is m, and the number of current ant is k, the pheromones on t moment side (i, j)
Concentration is Δ τij(t), ant k is every is by an iteration Pheromone update amount
S1022 obtains local information element according to the more new strategy of pheromones are as follows::
Wherein, Q is pheromones total amount, is a constant；LkFor the total distance passed through during current ant current iteration, θ is letter
Breath element increases the factor, and value range is (0.1)
Pheromone concentration on every road is arranged at [min, max] S1023, between, and pheromones volatilization is introduced auxiliary
Volatilization factor is helped, to global information element more new strategy are as follows:
Wherein, addition parameter ω ∈ (0,1) and γ ∈ (1,2), ω be the negative volatilization factor of pheromones, γ for pheromones just volatilizing because
Son.
S1024, when present road pheromone concentration is less than road information element concentration limit, system can be dense by present road pheromones
Degree is set to road information element concentration minimum value.
4. a kind of intelligent storage route recognition methods for more AGV according to claim 1, it is characterised in that: described
S200 further include:
S201 generates the time window for ideally describing AGV operating status according to AGV mission bit stream and scheduling scheme first,
Including occupying the information such as node, the AGV number in section, initial time, end time, time window length；
S202, by generating node time window, the mode for preventing AGV from generating overlapping in node time window avoids node conflict from sending out
It is raw, including conflict in opposite directions and right-angled intersection node conflict；
S203 sets AGV as at the uniform velocity to avoid coming up with conflict, by generating section time window, prevents AGV in the section time
The mode that window generates overlapping avoids section from conflicting in opposite directions generation；
S204 is ranked up the node time window and section time window of current all overlappings, and wherein section time window looks only for
The conllinear and contrary section of route.
S205 repeats the operation of S202, S203 and S204, until AGV equal nothing on all node time windows, section time window
Overlapping terminates to search update operation；
S206 counts the time that each AGV reaches destination node, take wherein the runing time longest time be used as scheduling scheme it is whole
The running body time.
5. a kind of intelligent storage route recognition methods for more AGV according to claim 1, it is characterised in that: described
S200 further include: the path runing time according to obtained in S200 performs the following operation:
S207 marks vehicle according to the adjacency matrix table of relationship between expression section and node serial number rule between generation during window
Between section position be parallel or vertical, thus according between the section of front and back position enquiring statistics scheduling scheme in AGV turn
Number；
S208, according to search overlapping time window after to time window into the statistics of the number ingeniously updated, to calculate dispatching party
The number that AGV is waited for parking in case；
S209 realizes scheduling scheme using following formula:
Wherein, trunFor runing time, aL is AGV vehicle commander, and av is AGV speed, trurnFor number of turns, SstopFor AGV parking time
Number.
6. a kind of intelligent storage route recognition methods for more AGV according to claim 1, it is characterised in that: described
In S300 further include:
S301 determines the boundary route range of each AGV first with the wireless sensor network inside AGV；
S302, then according to the reasonable cloth of requirement of the size of detection zone, site environment and positioning accuracy in boundary route range
Beaconing nodes are set, and are numbered, beaconing nodes library is formed；
S303 sends broadcast packet, all beaconing nodes hairs for receiving this broadcast packet to surrounding after node access networks network to be measured
Send broadcast packet to the node to be measured；
S304 sorts beaconing nodes according to the sequence of RSSI value from high to low, and preferably preceding n beaconing nodes are this positioning
Reference mode reads its number and corresponding RSSI value:
RSSI=Pi-PL(d)；
Wherein, PL (d) is the path loss after distance d, d0For reference distance,It is by reference distance d0Afterwards
Path loss.
7. a kind of intelligent storage route recognition methods for more AGV according to claim 1, it is characterised in that: described
In S400 further include:
S401, under workshop normal operating conditions, it is assumed that t moment system monitoring occur at some node to there are AGV therefore
Barrier, by test can be obtained normal condition dispatching system from monitor failure to calculate complete transmitting order to lower levels to AGV required for
Time t ', and inquire generate failure AGV the t+t ' moment locating time window；
S402, after there is AGV failure at workshop node, remaining is carrying out the AGV of scheduler task locating for the t+t ' moment
Time window have and only there are three types of, malfunctioning node is connected section time window, normal node time window and normal section time window；
S403, workshop dispatching system monitors there are AGV on some section to after breaking down, according to the reality of system environments
When monitoring information and the Real-time Feedback of AGV judge whether failure AGV task is completed, completion does not need New School's vehicle then, otherwise
Whether inquiry waits troubleshooting to complete, and waiting wouldn't then operate the task, and whether otherwise inquire has empty wagons, without not sending then, has
Then send out new car；
S404 inquires workshop environmental information and the operating status of AGV, etching system when determining t+t ' according to S402, S403
Scheduler task, and generate specific task scheduling table；
S405, when workshop node breaks down, the shortest path library failure that original generates offline is needed according to fault message weight
The section distance that node is connected is set as infinite by the new node relationships updated in workshop map, adjacency matrix, and to most short
The generating algorithm in path library adjusts, and generates the new offline path library for meeting workshop situation, timely responds to system change
S406, the improvement ant group algorithm model for calling static state workshop two-stage AGV scheduling to solve are calculated；
S407, after optimization, scheduling system generates optimal scheduling scheme, and exports the fitness value of the scheduling scheme, entirety
Runing time, stop frequency, number of turns and each AGV are in its planning path by node, the specific time window letter in section
Breath.
8. a kind of intelligent storage route identification device for claim 1 the method, it is characterised in that: including data acquisition
Module, database, data processing unit and AGV control module；
The data terminal and database of the data acquisition module interconnect, and the data acquisition module exports AGV scheduling information
It is handled to data processing unit；
The data processing unit realizes the processing to more AGV scheduling strategy information using improved Ant Colony System, and will processing
Information is transmitted to AGV control module；
The AGV control module identifies route according to the location information of the processing information and self-sensor device network received；
The communication port of the AGV control module, which also passes through wireless network connection, host computer, the control of the AGV control module
End also interactive connection has radio frequency control module.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910378416.XA CN110262408A (en) | 2019-05-08 | 2019-05-08 | A kind of intelligent storage route identification device and method for more AGV |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910378416.XA CN110262408A (en) | 2019-05-08 | 2019-05-08 | A kind of intelligent storage route identification device and method for more AGV |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110262408A true CN110262408A (en) | 2019-09-20 |
Family
ID=67914421
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910378416.XA Pending CN110262408A (en) | 2019-05-08 | 2019-05-08 | A kind of intelligent storage route identification device and method for more AGV |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110262408A (en) |
Cited By (22)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110941250A (en) * | 2019-12-06 | 2020-03-31 | 国网天津市电力公司电力科学研究院 | AGV flexible logistics control method for automatic loading and unloading of low-voltage metering box |
| CN111309028A (en) * | 2020-03-25 | 2020-06-19 | 安吉智能物联技术有限公司 | Material transportation method and system |
| CN111459108A (en) * | 2020-04-08 | 2020-07-28 | 北京理工大学 | Task allocation and conflict-free path planning method for pull-type multi-AGV system |
| CN111459171A (en) * | 2020-05-15 | 2020-07-28 | 合肥卓科智能技术有限公司 | Path optimization regulation and control system of AGV dolly |
| CN111650930A (en) * | 2020-04-09 | 2020-09-11 | 吉利汽车研究院（宁波）有限公司 | Automatic parking path planning method and device based on genetic algorithm |
| CN111653098A (en) * | 2020-06-04 | 2020-09-11 | 南京航空航天大学 | Intersection passing sequence optimization method for automatic guided vehicle with multiple loading capacity |
| CN111832816A (en) * | 2020-07-03 | 2020-10-27 | 浙江大学医学院附属妇产科医院 | Medical AGV group logistics regulation and control system and method based on scheduling algorithm |
| CN111913465A (en) * | 2020-06-19 | 2020-11-10 | 广东工业大学 | Fault real-time monitoring method and device for multi-intelligent-trolley system |
| CN112001646A (en) * | 2020-08-27 | 2020-11-27 | 上海汽车集团股份有限公司 | Material scheduling method and device, storage medium and electronic equipment |
| CN112000113A (en) * | 2020-06-19 | 2020-11-27 | 南京理工大学 | Multi-AGV storage management system and method based on traditional Chinese medicine pharmacy |
| CN112001541A (en) * | 2020-08-24 | 2020-11-27 | 南京理工大学 | Improved genetic algorithm for path optimization |
| CN112749927A (en) * | 2021-02-03 | 2021-05-04 | 香港中文大学（深圳） | Dispatching method of storage robot and related equipment |
| CN113256713A (en) * | 2021-06-10 | 2021-08-13 | 浙江华睿科技有限公司 | Pallet position identification method and device, electronic equipment and storage medium |
| CN113433896A (en) * | 2021-05-24 | 2021-09-24 | 深圳大学 | Dynamic production scheduling method and system |
| CN113778097A (en) * | 2021-09-15 | 2021-12-10 | 龙岩学院 | Intelligent warehouse logistics robot path planning method for improving A-STAR algorithm through L-shaped path trend |
| CN113848828A (en) * | 2021-09-08 | 2021-12-28 | 广州杰赛科技股份有限公司 | Automatic obstacle avoidance control method, device, equipment and medium for AGV trolley |
| CN113955362A (en) * | 2021-08-11 | 2022-01-21 | 浙江立镖机器人有限公司 | Three-dimensional sorting control method, three-dimensional sorting robot and related equipment |
| CN114063584A (en) * | 2021-11-19 | 2022-02-18 | 江苏科技大学 | Scheduling control method, device and system for integrated processing of ship weight-related parts |
| CN114442627A (en) * | 2022-01-24 | 2022-05-06 | 电子科技大学 | Dynamic desktop path finding system and method for smart home mobile device |
| CN114842641A (en) * | 2022-03-11 | 2022-08-02 | 华设设计集团股份有限公司 | Provincial-domain-oriented multi-mode chain type traffic distribution method |
| CN116501063A (en) * | 2022-11-22 | 2023-07-28 | 山东卓越精工集团有限公司 | AGV dolly control system based on concrete transportation platform |
| CN116542417A (en) * | 2023-07-05 | 2023-08-04 | 泓浒(苏州)半导体科技有限公司 | Control system and method for semiconductor production line conveying system |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2004280213A (en) * | 2003-03-13 | 2004-10-07 | Japan Science & Technology Agency | Distributed path planning device and method, and distributed path planning program |
| WO2015108819A1 (en) * | 2014-01-14 | 2015-07-23 | Qualcomm Incorporated | Connectivity maintenance using a quality of service-based robot path planning algorithm |
| CN107036618A (en) * | 2017-05-24 | 2017-08-11 | 合肥工业大学(马鞍山)高新技术研究院 | A kind of AGV paths planning methods based on shortest path depth optimization algorithm |
| CN108958257A (en) * | 2018-07-25 | 2018-12-07 | 深圳市集大自动化有限公司 | The collaboration paths planning method of more AGV integrated navigations |
-
2019
- 2019-05-08 CN CN201910378416.XA patent/CN110262408A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2004280213A (en) * | 2003-03-13 | 2004-10-07 | Japan Science & Technology Agency | Distributed path planning device and method, and distributed path planning program |
| WO2015108819A1 (en) * | 2014-01-14 | 2015-07-23 | Qualcomm Incorporated | Connectivity maintenance using a quality of service-based robot path planning algorithm |
| CN107036618A (en) * | 2017-05-24 | 2017-08-11 | 合肥工业大学(马鞍山)高新技术研究院 | A kind of AGV paths planning methods based on shortest path depth optimization algorithm |
| CN108958257A (en) * | 2018-07-25 | 2018-12-07 | 深圳市集大自动化有限公司 | The collaboration paths planning method of more AGV integrated navigations |
Non-Patent Citations (3)
| Title |
|---|
| 徐燕等: "无线传感器网络中基于RSSI差值的改进定位算法", 《厦门大学学报(自然科学版)》 * |
| 汪先超: "《中国优秀硕士学位论文全文数据库信息科技辑》", 15 March 2017 * |
| 许向阳等: "基于改进蚁群算法的消防路线规划***", 《通信技术》 * |
Cited By (33)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110941250A (en) * | 2019-12-06 | 2020-03-31 | 国网天津市电力公司电力科学研究院 | AGV flexible logistics control method for automatic loading and unloading of low-voltage metering box |
| CN111309028A (en) * | 2020-03-25 | 2020-06-19 | 安吉智能物联技术有限公司 | Material transportation method and system |
| CN111309028B (en) * | 2020-03-25 | 2022-11-29 | 安吉智能物联技术有限公司 | Material transportation method and system |
| CN111459108A (en) * | 2020-04-08 | 2020-07-28 | 北京理工大学 | Task allocation and conflict-free path planning method for pull-type multi-AGV system |
| CN111650930A (en) * | 2020-04-09 | 2020-09-11 | 吉利汽车研究院（宁波）有限公司 | Automatic parking path planning method and device based on genetic algorithm |
| CN111459171A (en) * | 2020-05-15 | 2020-07-28 | 合肥卓科智能技术有限公司 | Path optimization regulation and control system of AGV dolly |
| CN111653098A (en) * | 2020-06-04 | 2020-09-11 | 南京航空航天大学 | Intersection passing sequence optimization method for automatic guided vehicle with multiple loading capacity |
| CN111653098B (en) * | 2020-06-04 | 2022-04-26 | 南京航空航天大学 | Intersection passing sequence optimization method for automatic guided vehicle with multiple loading capacity |
| CN111913465A (en) * | 2020-06-19 | 2020-11-10 | 广东工业大学 | Fault real-time monitoring method and device for multi-intelligent-trolley system |
| CN112000113A (en) * | 2020-06-19 | 2020-11-27 | 南京理工大学 | Multi-AGV storage management system and method based on traditional Chinese medicine pharmacy |
| CN111832816A (en) * | 2020-07-03 | 2020-10-27 | 浙江大学医学院附属妇产科医院 | Medical AGV group logistics regulation and control system and method based on scheduling algorithm |
| CN112001541A (en) * | 2020-08-24 | 2020-11-27 | 南京理工大学 | Improved genetic algorithm for path optimization |
| CN112001541B (en) * | 2020-08-24 | 2022-10-11 | 南京理工大学 | Path optimization method based on improved genetic algorithm |
| CN112001646A (en) * | 2020-08-27 | 2020-11-27 | 上海汽车集团股份有限公司 | Material scheduling method and device, storage medium and electronic equipment |
| CN112749927A (en) * | 2021-02-03 | 2021-05-04 | 香港中文大学（深圳） | Dispatching method of storage robot and related equipment |
| CN112749927B (en) * | 2021-02-03 | 2023-11-28 | 香港中文大学（深圳） | Scheduling method of storage robot and related equipment |
| CN113433896A (en) * | 2021-05-24 | 2021-09-24 | 深圳大学 | Dynamic production scheduling method and system |
| CN113256713A (en) * | 2021-06-10 | 2021-08-13 | 浙江华睿科技有限公司 | Pallet position identification method and device, electronic equipment and storage medium |
| CN113256713B (en) * | 2021-06-10 | 2021-10-15 | 浙江华睿科技股份有限公司 | Pallet position identification method and device, electronic equipment and storage medium |
| CN113955362B (en) * | 2021-08-11 | 2024-02-06 | 浙江立镖机器人有限公司 | Stereoscopic sorting control method, stereoscopic sorting robot and related equipment |
| CN113955362A (en) * | 2021-08-11 | 2022-01-21 | 浙江立镖机器人有限公司 | Three-dimensional sorting control method, three-dimensional sorting robot and related equipment |
| CN113848828A (en) * | 2021-09-08 | 2021-12-28 | 广州杰赛科技股份有限公司 | Automatic obstacle avoidance control method, device, equipment and medium for AGV trolley |
| CN113778097B (en) * | 2021-09-15 | 2023-05-19 | 龙岩学院 | Intelligent warehouse logistics robot path planning method based on L-shaped path trend improved A-STAR algorithm |
| CN113778097A (en) * | 2021-09-15 | 2021-12-10 | 龙岩学院 | Intelligent warehouse logistics robot path planning method for improving A-STAR algorithm through L-shaped path trend |
| CN114063584A (en) * | 2021-11-19 | 2022-02-18 | 江苏科技大学 | Scheduling control method, device and system for integrated processing of ship weight-related parts |
| CN114063584B (en) * | 2021-11-19 | 2024-04-26 | 江苏科技大学 | Scheduling control method, device and system for integrated processing of ship weight closing parts |
| CN114442627A (en) * | 2022-01-24 | 2022-05-06 | 电子科技大学 | Dynamic desktop path finding system and method for smart home mobile device |
| CN114442627B (en) * | 2022-01-24 | 2023-10-13 | 电子科技大学 | Dynamic desktop path finding system and method for intelligent home mobile equipment |
| CN114842641A (en) * | 2022-03-11 | 2022-08-02 | 华设设计集团股份有限公司 | Provincial-domain-oriented multi-mode chain type traffic distribution method |
| CN114842641B (en) * | 2022-03-11 | 2024-02-09 | 华设设计集团股份有限公司 | Multi-mode chain traffic distribution method for province domain |
| CN116501063A (en) * | 2022-11-22 | 2023-07-28 | 山东卓越精工集团有限公司 | AGV dolly control system based on concrete transportation platform |
| CN116542417A (en) * | 2023-07-05 | 2023-08-04 | 泓浒(苏州)半导体科技有限公司 | Control system and method for semiconductor production line conveying system |
| CN116542417B (en) * | 2023-07-05 | 2023-09-12 | 泓浒(苏州)半导体科技有限公司 | Control system and method for semiconductor production line conveying system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110262408A (en) | A kind of intelligent storage route identification device and method for more AGV | |
| Saad et al. | Hedonic coalition formation for distributed task allocation among wireless agents | |
| CN109634187A (en) | A kind of AGV remote monitoring system | |
| CN110210806B (en) | Cloud-based unmanned vehicle frame structure with 5G edge calculation function and control evaluation method thereof | |
| CN103256931B (en) | Visual navigation system of unmanned planes | |
| CN111539574B (en) | Order dispatching method and system for multiple robots | |
| Chen et al. | A machine-learning based time constrained resource allocation scheme for vehicular fog computing | |
| CN104662847A (en) | Gateway apparatus and data processing method | |
| CN110244712A (en) | A kind of more AGV system paths planning methods | |
| Kopeikin et al. | Multi-uav network control through dynamic task allocation: Ensuring data-rate and bit-error-rate support | |
| CN110446184A (en) | A kind of car networking method for routing of multi-mode switching | |
| CN113965948A (en) | Sensor data acquisition method based on self-adaptive clustering network | |
| KR20190138294A (en) | Method for Cloud-Based Drone Navigation for Efficient Battery Charging in Drone Networks | |
| CN102104542A (en) | Method for realizing service group router under forwarding and controlling separated network architecture | |
| CN115017658A (en) | Rail transit network optimization method based on reachability and node importance | |
| CN113885466A (en) | AGV scheduling algorithm simulation system | |
| Romaniuk et al. | Objective control functions of FANET communication nodes of land-air network | |
| CN117726153B (en) | Real-time re-planning method suitable for unmanned aerial vehicle cluster operation tasks | |
| Bilen et al. | Digital twin evolution for hard-to-follow aeronautical ad-hoc networks in beyond 5g | |
| CN108280530A (en) | The unmanned driving handling method for optimizing route in reservoir area and system under two-way approach asymmetrical state | |
| Kasturi et al. | An improved energy efficient solution for routing in IoT | |
| CN115790594A (en) | Multi-type robot path planning method, device, equipment and storage medium | |
| CN110855507B (en) | Unmanned data network interaction method based on software definition | |
| CN111582579A (en) | Predictive multi-AGV task allocation method based on topological map | |
| Yu et al. | Reinforcement learning based prioritized cross-area resource management for vehicular cloud networks |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20190920 |